Networks of range finders are a popular tool for monitoring large cluttered areas and for tracking people. Whenever multiple scanners are used for this purpose, one major challenge is how to determine the relative positions of all of the scanners.
Range finders, in particular laser range finders, are widely used sensors in robotics and in industry. Applications typically include the task of acquiring spatial information about the environment, of avoiding obstacles, of building maps or to localize a mobile robot in a map, to name only a few. Furthermore, they have been used to track people, to monitor areas or to implement safety zones, i.e., to detect humans and switch off dangerous machines when the humans are too close.
For the task of surveying a larger area, typically a network of multiple range finders is necessary. At the same time, efforts are made to minimize the number of sensors in the network for economic reasons. This can lead to the reduction of the overlap between the visible areas of the individual range finders.
Calibrating such a sensor network can be tedious, especially when the overlap between the visible areas of the individual scanners is small. In some conventional calibration algorithms, predefined patterns or calibration aids are used. Such calibration patterns may improve the data association between individual observations. However, such calibration patterns may not be helpful if the range finders to be calibrated do not perform a visual measurement.